Type: Package
Title: Small Area Estimation Non-Parametric Based Nadaraya-Watson Kernel
Version: 0.1.1
Author: Wicak Surya Hasani[aut, cre], Azka Ubaidillah[aut]
Maintainer: Wicak Surya Hasani <221710052@stis.ac.id>
Description: Propose an area-level, non-parametric regression estimator based on Nadaraya-Watson kernel on small area mean. Adopt a two-stage estimation approach proposed by Prasad and Rao (1990). Mean Squared Error (MSE) estimators are not readily available, so resampling method that called bootstrap is applied. This package are based on the model proposed in Two stage non-parametric approach for small area estimation by Pushpal Mukhopadhyay and Tapabrata Maiti(2004) http://www.asasrms.org/Proceedings/y2004/files/Jsm2004-000737.pdf.
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.0.2
URL: https://github.com/wicaksh/saekernel
BugReports: https://github.com/wicaksh/saekernel/issues
Suggests: knitr, rmarkdown, covr
VignetteBuilder: knitr
Imports: stats
Depends: R (≥ 2.10)
NeedsCompilation: no
Packaged: 2021-06-03 17:25:03 UTC; User
Repository: CRAN
Date/Publication: 2021-06-04 07:40:02 UTC

Sample Data for Small Area Estimation Non-Parametric Based Nadaraya-Watson Kernel

Description

Dataset to Simulate Small Area Estimation Non-Parametric Based Nadaraya-Watson Kernel

This data is generated by these following steps:

  1. Generate explanatory variables Vardir. Vardir ~ abs(N(0, 0.1))
    Generate explanatory variables x. x ~ U(min=0, max=1)
    Calculate direct estimation y where y_{i} = sin(2 * \pi * x^3) + 5

  2. Then combine the direct estimations y, auxiliary variables x, and sampling varians Vardir in a dataframe then named as Data_saekernel

Usage

Data_saekernel

Format

A data frame with 100 rows and 3 variables:

y

Direct Estimation of Y

x

Auxiliary Variable of X

Vardir

Sampling Variance of Y

Small Area Estimation Non-Parametric Based Nadaraya-Watson Kernel and Bootstrap Mean Squared Error Estimators

Description

This Function Gives Small Area Estimation Non-Parametric Based Nadaraya-Watson Kernel and Calculates The Bootstrap Mean Squared Error Estimates

Usage

mse_saekernel(X, Y, vardir, bandwidth, B = 1000)

Arguments

X

Auxiliary Variable of X

Y

Direct Estimation of Y

vardir

Sampling Variances of Direct Estimators

bandwidth

The kernel Bandwidth Smoothing Parameter

B

Number of Bootstrap. Default is 1000

Value

This function returns a list with following objects:

est

a value of Small Area Estimation Non-Parametric Based Nadaraya-Watson Kernel

refvar

Estimated Random Effect Variance

mse

Bootstrap Mean Squared Error Estimators of Small Area Estimation Non-Parametric Based Nadaraya-Watson Kernel

Examples

##load dataset
data(Data_saekernel)

mse_saekernel(X = Data_saekernel$x, Y = Data_saekernel$y,
vardir = Data_saekernel$Vardir, bandwidth = 0.04, B = 1000)

Small Area Estimation Non-Parametric Based Nadaraya-Watson Kernel

Description

This Function Gives Small Area Estimation Non-Parametric Based Nadaraya-Watson Kernel

Usage

saekernel(X, Y, vardir, bandwidth)

Arguments

X

Auxiliary Variable of X

Y

Direct Estimation of Y

vardir

Sampling variances of Direct Estimators

bandwidth

The kernel Bandwidth Smoothing Parameter

Value

This function returns a list with following objects:

est

a value of Small Area Estimation Non-Parametric Based Nadaraya-Watson Kernel

refvar

Estimated Random Effect Variance

Examples

##load dataset
data(Data_saekernel)

saekernel(X = Data_saekernel$x, Y = Data_saekernel$y,
vardir = Data_saekernel$Vardir, bandwidth = 0.04)